Fingerprint prediction using statistical and machine learning methods

Promise Molale*, Bhekisipho Twala, Solly Seeletse

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


There are various fingerprint prediction models but it is difficult to determine which model is ideal based on one performance measure. This paper looked at four machine learning methods and one statistical method: k-Nearest Neighbour (k-NN), Artificial Neural Network (ANN), Decision Trees (DT), Support Vector Machine (SVM) and Linear Regression (LR). The performance of the classifiers is evaluated in terms of the following performance measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Correlation Coefficient (CC) and time taken to build the model. The assessment was done using the National Institute of Standards and Technology (NIST) fingerprint image database. Examining the performance of the classifiers showed DT method to be better than all other compared methods.

Original languageEnglish
Pages (from-to)311-316
Number of pages6
JournalICIC Express Letters
Issue number2
Publication statusPublished - 2013
Externally publishedYes


  • Classifier
  • Fingerprint
  • Machine leaning
  • Performance measures
  • Prediction


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